用少样本数据提升管道缺陷分割精度，兼顾性能与效率。

Culverts and sewer pipes are critical components of drainage systems, and their failure can lead to serious risks to public safety and the environment. In this thesis, we explore methods to improve automated defect segmentation in culverts and sewer pipes. Collecting and annotating data in this field is cumbersome and requires domain knowledge. Having a large dataset for structural defect detection is therefore not feasible. Our proposed methods are tested under conditions with limited annotated data to demonstrate applicability to real-world scenarios. Overall, this thesis proposes three methods to significantly enhance defect segmentation and handle data scarcity. This can be addressed either by enhancing the training data or by adjusting a models architecture. First, we evaluate preprocessing strategies, including traditional data augmentation and dynamic label injection. These techniques significantly improve segmentation performance, increasing both Intersection over Union (IoU) and F1 score. Second, we introduce FORTRESS, a novel architecture that combines depthwise separable convolutions, adaptive Kolmogorov-Arnold Networks (KAN), and multi-scale attention mechanisms. FORTRESS achieves state-of-the-art performance on the culvert sewer pipe defect dataset, while significantly reducing the number of trainable parameters, as well as its computational cost. Finally, we investigate few-shot semantic segmentation and its applicability to defect detection. Few-shot learning aims to train models with only limited data available. By employing a bidirectional prototypical network with attention mechanisms, the model achieves richer feature representations and achieves satisfactory results across evaluation metrics.